October 30, 2024 • 6 mins
An Auckland start-up is making waves in the United States.
ListAssist has won a contract with real estate firm Howard Hanna, the largest independent firm in the country.
They’ve created an AI tool that allows you to type your specific requirements into a search bar, which will then bring up the relevant options.
Founder Chris McGoldrick told Mike Hosking that they essentially overtake the existing search bars on real estate websites and enriching the data.
He says they build upon the information given by the agents, adding their own to build as comprehensive a picture as possible.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
List assist from startup that's making it big in the States,
users AI to allow natural language queries for real estate listings.
The founder, Chris McGoldrick, is well, the it's Chris morning
to you.
Speaker 2 (00:11):
Good morning. How are you.
Speaker 1 (00:12):
I'm very well? Indeed, Now I featured you on the program,
and I think you probably heard it some way, somehow,
And my suggestion was, were you personally offended by what
I said? Were you deeply upset Chris by my analysis
of your company or what happened there.
Speaker 2 (00:25):
Look, I was very oppressive of it. I had a
couple of friends and family reach out and say that
you mentioned a list of system and a few details around.
You know, you mentioned what would do that perhaps weren't
so accurate. I thought it was great. I think we
could do some great in commercials together. You starting with
the germ and gloom of why it could never work,
then me coming in and talking about all the things
we've done to bring it to life. So I'm all
(00:47):
for it. So was it? First?
Speaker 1 (00:49):
We'll let me ask you some some startup questions. Was
raising money hard or not?
Speaker 2 (00:55):
I think it's always really hard, to be honest, And
you know, we launched as kind of late twenty twenty two,
so it was sort of after I SATs the boom
period of late twenty twenty early twenty twenty one. So
it has been hard throughout the process, but we were
fortunate the first trip that I went to the US,
we got investment from the top Remax owner in the world,
(01:15):
and that gave us some really good initial credibility over there,
which has made that process a little bit easier over time.
Speaker 1 (01:20):
And what do you see your growth pass as? Is
it exponential or you got a plan or what?
Speaker 2 (01:26):
Yeah? Absolutely, I mean we're still really focused on just
putting one foot in front of the other and kind
of executing a lot of these recent deals that we've
boughked to life. So not getting ahead of ourselves with that,
but yeah, certainly a planned for exponential growth over time.
Speaker 1 (01:38):
Have you built the AI part of it that's unique
to you? Do you in other words, do you do
something no one else can do? Or is this just
a bit of AI that someone else will think of
eventually anyway?
Speaker 2 (01:49):
No, so we've kind of built a lot of our
own technology. And one of the things that you reference
was these sort of things only being as good as
the information given from the agent, and that kind of
being the barrier is to buy this maybe wouldn't become
as effective as you might hope. What we do is
we enrich the data with things like our own computer
vision that we've built, which is our ability to understand
(02:10):
property images. We've built our own AI models too, which
enable us to create another layer of data. We also
incorporate things like locational data. I know you mentioned stuff
like you wouldn't be able to say close to this
school or close to this restaurant. All of that capability
we have at the moment, and our approach is a
little bit different. So we actually go to existing real
(02:30):
estate websites and essentially take over their search and enable
this AI search on their website. So it's slightly different
to some of the other type of approaches we've seen
around search.
Speaker 1 (02:40):
Was I correct though, in saying that no matter where
you get your material from, it's only as rich as
that material pool of the pool doesn't have what I'm
looking for, it can't bring it to me.
Speaker 2 (02:52):
No, because again, we enrich the data so that an
agent may overlook the fact that it's got a pool
or a certain type of backyard as where you ingest
the data and process things like the images. We add
all these different layers of data to it which enable
us to essentially match them to what a user is
asking for.
Speaker 1 (03:10):
How much labors involved in that? In other words, you're
taking what I was trying to say was you're looking
potentially to include every single piece of detail so that
no matter what I ask, it'll go, I've got the
house right there for you. So how much input does
that require?
Speaker 2 (03:27):
Yeah, so in terms of a consumer asking or in
terms of.
Speaker 1 (03:30):
No, in terms of you creating the information that's so
rich and so detailed that no matter what I ask,
it's got a good accurate answer.
Speaker 2 (03:41):
Yeah. So obviously, as I mentioned, we've been going for
a couple of years and building throughout that time, so
it has taken a long time. It's one thing too
that it's never kind of finished, you know, So as
we've kind of I think where you might have seen
us as in that story that we've taken over search
for the largest independent brokerage in America, and so things
like understanding how their users are actually using it, the
(04:03):
types of searchers that enables us to continue to iterate
and get better and understand the areas that we need
to improve on and the evolution that we need to,
you know, kind of bring to our models.
Speaker 1 (04:15):
Will you get to a point and I'm asking from
ignorance here, I don't know whether it's doable. I see
a house that I quite like. Will there come a
day when I can somehow show AI or your tool
this photo, see this house, AI show me seventeen virtually the.
Speaker 2 (04:30):
Same, absolutely, and there's stuff like that kind of you know,
not too far away. One of the progressions that we're
working on is as you go to let's say one
of our American clients and you say, I want a
red house with a blue door and a big backyard
near that school. As we start showing you properties, you'll
soon be able to go, well, I don't like that kitchen,
I like that backyard. And what that does is continues
(04:53):
to build information on you at lead, which gets passed
on to agents. So by the time an agent gets
kind of your information instead of a traditional lead which
knows Mike's interested in five Smith Street or whatever it
may be, we now know Mike isn't like these bathrooms,
he likes kitchens that look like this. He wants to
be in this area. So it just builds so much
efficiency in that sales cycle.
Speaker 1 (05:15):
Are you a AI freak or are you just an
entrepreneur who's who's seen an opportunity here just.
Speaker 2 (05:21):
In the more entrepreneurial path I would I would think
trying to be an AI freak, but probably more in
the entrepreneurial side of that.
Speaker 1 (05:28):
Right, So where are you at in the broad based
global discussion that AI is going to tip us upside
down and change the world in which we know versus
it'll probably do some cool stuff, but we've overreacted.
Speaker 2 (05:38):
Yeah, Look, I'm probably more in that it'll probably do
some cool stuff and we've overreacted. It's been a really
interesting learning curve for us. I think there's been so
much hype around it and a lot of the discussions
that we've had with with different people in America, they've
been like, look, I just kept seeing solutions to problems.
I don't think I have. People are so quick to
bring these products to market without you know, real life
(05:59):
use cases. So you know, for us, we've been hyper
focused on real world proof points. You know. So we
got a couple of really good clients early on and
just nailing that execution and bringing value and generating real
rois kind of in what our focus has been on,
and that's proved pretty effective so far in.
Speaker 1 (06:15):
Terms to go well with it. Might appreciate it very much.
Chris mcgoldwick, who is the founder of list assist out
of the States.
Speaker 2 (06:22):
For more from the Mic Asking Breakfast listen live to
news talks.
Speaker 1 (06:26):
It'd be from six am weekdays, or follow the podcast
on iHeartRadio.
List assist from startup that's making it big in the States,
users AI to allow natural language queries for real estate listings.
The founder, Chris McGoldrick, is well, the it's Chris morning
to you.
Speaker 2 (00:11):
Good morning. How are you.
Speaker 1 (00:12):
I'm very well? Indeed, Now I featured you on the program,
and I think you probably heard it some way, somehow,
And my suggestion was, were you personally offended by what
I said? Were you deeply upset Chris by my analysis
of your company or what happened there.
Speaker 2 (00:25):
Look, I was very oppressive of it. I had a
couple of friends and family reach out and say that
you mentioned a list of system and a few details around.
You know, you mentioned what would do that perhaps weren't
so accurate. I thought it was great. I think we
could do some great in commercials together. You starting with
the germ and gloom of why it could never work,
then me coming in and talking about all the things
we've done to bring it to life. So I'm all
(00:47):
for it. So was it? First?
Speaker 1 (00:49):
We'll let me ask you some some startup questions. Was
raising money hard or not?
Speaker 2 (00:55):
I think it's always really hard, to be honest, And
you know, we launched as kind of late twenty twenty two,
so it was sort of after I SATs the boom
period of late twenty twenty early twenty twenty one. So
it has been hard throughout the process, but we were
fortunate the first trip that I went to the US,
we got investment from the top Remax owner in the world,
(01:15):
and that gave us some really good initial credibility over there,
which has made that process a little bit easier over time.
Speaker 1 (01:20):
And what do you see your growth pass as? Is
it exponential or you got a plan or what?
Speaker 2 (01:26):
Yeah? Absolutely, I mean we're still really focused on just
putting one foot in front of the other and kind
of executing a lot of these recent deals that we've
boughked to life. So not getting ahead of ourselves with that,
but yeah, certainly a planned for exponential growth over time.
Speaker 1 (01:38):
Have you built the AI part of it that's unique
to you? Do you in other words, do you do
something no one else can do? Or is this just
a bit of AI that someone else will think of
eventually anyway?
Speaker 2 (01:49):
No, so we've kind of built a lot of our
own technology. And one of the things that you reference
was these sort of things only being as good as
the information given from the agent, and that kind of
being the barrier is to buy this maybe wouldn't become
as effective as you might hope. What we do is
we enrich the data with things like our own computer
vision that we've built, which is our ability to understand
(02:10):
property images. We've built our own AI models too, which
enable us to create another layer of data. We also
incorporate things like locational data. I know you mentioned stuff
like you wouldn't be able to say close to this
school or close to this restaurant. All of that capability
we have at the moment, and our approach is a
little bit different. So we actually go to existing real
(02:30):
estate websites and essentially take over their search and enable
this AI search on their website. So it's slightly different
to some of the other type of approaches we've seen
around search.
Speaker 1 (02:40):
Was I correct though, in saying that no matter where
you get your material from, it's only as rich as
that material pool of the pool doesn't have what I'm
looking for, it can't bring it to me.
Speaker 2 (02:52):
No, because again, we enrich the data so that an
agent may overlook the fact that it's got a pool
or a certain type of backyard as where you ingest
the data and process things like the images. We add
all these different layers of data to it which enable
us to essentially match them to what a user is
asking for.
Speaker 1 (03:10):
How much labors involved in that? In other words, you're
taking what I was trying to say was you're looking
potentially to include every single piece of detail so that
no matter what I ask, it'll go, I've got the
house right there for you. So how much input does
that require?
Speaker 2 (03:27):
Yeah, so in terms of a consumer asking or in
terms of.
Speaker 1 (03:30):
No, in terms of you creating the information that's so
rich and so detailed that no matter what I ask,
it's got a good accurate answer.
Speaker 2 (03:41):
Yeah. So obviously, as I mentioned, we've been going for
a couple of years and building throughout that time, so
it has taken a long time. It's one thing too
that it's never kind of finished, you know, So as
we've kind of I think where you might have seen
us as in that story that we've taken over search
for the largest independent brokerage in America, and so things
like understanding how their users are actually using it, the
(04:03):
types of searchers that enables us to continue to iterate
and get better and understand the areas that we need
to improve on and the evolution that we need to,
you know, kind of bring to our models.
Speaker 1 (04:15):
Will you get to a point and I'm asking from
ignorance here, I don't know whether it's doable. I see
a house that I quite like. Will there come a
day when I can somehow show AI or your tool
this photo, see this house, AI show me seventeen virtually the.
Speaker 2 (04:30):
Same, absolutely, and there's stuff like that kind of you know,
not too far away. One of the progressions that we're
working on is as you go to let's say one
of our American clients and you say, I want a
red house with a blue door and a big backyard
near that school. As we start showing you properties, you'll
soon be able to go, well, I don't like that kitchen,
I like that backyard. And what that does is continues
(04:53):
to build information on you at lead, which gets passed
on to agents. So by the time an agent gets
kind of your information instead of a traditional lead which
knows Mike's interested in five Smith Street or whatever it
may be, we now know Mike isn't like these bathrooms,
he likes kitchens that look like this. He wants to
be in this area. So it just builds so much
efficiency in that sales cycle.
Speaker 1 (05:15):
Are you a AI freak or are you just an
entrepreneur who's who's seen an opportunity here just.
Speaker 2 (05:21):
In the more entrepreneurial path I would I would think
trying to be an AI freak, but probably more in
the entrepreneurial side of that.
Speaker 1 (05:28):
Right, So where are you at in the broad based
global discussion that AI is going to tip us upside
down and change the world in which we know versus
it'll probably do some cool stuff, but we've overreacted.
Speaker 2 (05:38):
Yeah, Look, I'm probably more in that it'll probably do
some cool stuff and we've overreacted. It's been a really
interesting learning curve for us. I think there's been so
much hype around it and a lot of the discussions
that we've had with with different people in America, they've
been like, look, I just kept seeing solutions to problems.
I don't think I have. People are so quick to
bring these products to market without you know, real life
(05:59):
use cases. So you know, for us, we've been hyper
focused on real world proof points. You know. So we
got a couple of really good clients early on and
just nailing that execution and bringing value and generating real
rois kind of in what our focus has been on,
and that's proved pretty effective so far in.
Speaker 1 (06:15):
Terms to go well with it. Might appreciate it very much.
Chris mcgoldwick, who is the founder of list assist out
of the States.
Speaker 2 (06:22):
For more from the Mic Asking Breakfast listen live to
news talks.
Speaker 1 (06:26):
It'd be from six am weekdays, or follow the podcast
on iHeartRadio.
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